Biomedical subjects

Csaba Szántai-Kis

Publications and source records attributed to Csaba Szántai-Kis.

3 recordsLinked to original sources

PubMed2005

Drug discovery in the kinase inhibitory field using the Nested Chemical Library technology.

Kinase inhibitors are at the forefront of modern drug research, where mostly three technologies are used for hit-and-lead finding: high throughput screening of random libraries, three-dimensional structure-based drug design based on X-ray data, and focused libraries around limited number of new cores. Our novel Nested Chemical Library (NCL) (Vichem Chemie Research Ltd., Budapest, Hungary) technology is based on a knowledge base approach, where focused libraries around selected cores are used to generate pharmacophore models. NCL was designed on the platform of a diverse kinase inhibitory library organized around 97 core structures. We have established a unique, proprietary kinase inhibitory chemistry around these core structures with small focused sublibraries around each core. All the compounds in our NCL library are stored in a big unified Structured Query Language database along with their measured and calculated physicochemical and ADME/toxicity (ADMET) properties, together with thousands of molecular descriptors calculated for each compound. Biochemical kinase inhibitory assays on selected, cloned kinase enzymes for a few hundred NCL compound sets can provide sufficient biological data for rational computerized design of new analogues, based on our pharmacophore model-generating 3DNET4W QSPAR (quantitative structure-property/activity relationships) approach. Using this pharmacophore modeling approach and the ADMET filters, we can preselect synthesizable compounds for hit-and-lead optimization. Starting from this point and integrating the information from QSPAR, high-quality leads can be generated within a small number of optimization cycles. Applying NCL technology we have developed lead compounds for several validated kinase targets.

Algorithms↗

PubMed2004

Comparison of predictive ability of water solubility QSPR models generated by MLR, PLS and ANN methods.

ADME/Tox computational screening is one of the most hot topics of modern drug research. About one half of the potential drug candidates fail because of poor ADME/Tox properties. Since the experimental determination of water solubility is time-consuming also, reliable computational predictions are needed for the pre-selection of acceptable "drug-like" compounds from diverse combinatorial libraries. Recently many successful attempts were made for predicting water solubility of compounds. A comprehensive review of previously developed water solubility calculation methods is presented here, followed by the description of the solubility prediction method designed and used in our laboratory. We have selected carefully 1381 compounds from scientific publications in a unified database and used this dataset in the calculations. The externally validated models were based on calculated descriptors only. The aim of model optimization was to improve repeated evaluations statistics of the predictions and effective descriptor scoring functions were used to facilitate quick generation of multiple linear regression analysis (MLR), partial least squares method (PLS) and artificial neural network (ANN) models with optimal predicting ability. Standard error of prediction of the best model generated with ANN (with 39-7-1 network structure) was 0.72 in logS units while the cross validated squared correlation coefficient (Q(2)) was better than 0.85. These values give a good chance for successful pre-selection of screening compounds from virtual libraries, based on the predicted water solubility.

Chemical Phenomena↗

PubMed2003

Validation subset selections for extrapolation oriented QSPAR models.

One of the most important features of QSPAR models is their predictive ability. The predictive ability of QSPAR models should be checked by external validation. In this work we examined three different types of external validation set selection methods for their usefulness in in-silico screening. The usefulness of the selection methods was studied in such a way that: 1) We generated thousands of QSPR models and stored them in 'model banks'. 2) We selected a final top model from the model banks based on three different validation set selection methods. 3) We predicted large data sets, which we called 'chemical universe sets', and calculated the corresponding SEPs. The models were generated from small fractions of the available water solubility data during a GA Variable Subset Selection procedure. The external validation sets were constructed by random selections, uniformly distributed selections or by perimeter-oriented selections. We found that the best performing models on the perimeter-oriented external validation sets usually gave the best validation results when the remaining part of the available data was overwhelmingly large, i.e., when the model had to make a lot of extrapolations. We also compared the top final models obtained from external validation set selection methods in three independent and different sizes of 'chemical universe sets'.

Computer Simulation↗